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The Rate of Return on Everything, 1870–2015?
Òscar Jordà† Katharina Knoll ‡ Dmitry Kuvshinov §
Moritz Schularick ¶ Alan M. Taylorz
November 2017
Abstract
This paper answers fundamental questions that have preoccupied modern economicthought since the 18th century. What is the aggregate real rate of return in the economy?Is it higher than the growth rate of the economy and, if so, by how much? Is there atendency for returns to fall in the long-run? Which particular assets have the highestlong-run returns? We answer these questions on the basis of a new and comprehensivedataset for all major asset classes, including—for the first time—total returns to thelargest, but oft ignored, component of household wealth, housing. The annual data ontotal returns for equity, housing, bonds, and bills cover 16 advanced economies from1870 to 2015, and our new evidence reveals many new insights and puzzles.
?This work is part of a larger project kindly supported by research grants from the Bundesministerium fürBildung und Forschung (BMBF) and the Institute for New Economic Thinking. We are indebted to a largenumber of researchers who helped with data on individual countries. We are especially grateful to FranciscoAmaral for outstanding research assistance, and would also like to thank Felix Rhiel, Mario Richarz, ThomasSchwarz and Lucie Stoppok for research assistance on large parts of the project. For their helpful commentswe thank Roger Farmer, Philipp Hofflin, David Le Bris, Emi Nakamura, Thomas Piketty, Matthew Rognlie,Jón Steinsson, Clara Martı́nez-Toledano Toledano, Stijn Van Nieuwerburgh, and conference participants at theNBER Summer Institute EFG Program Meeting and the Bank of England. All errors are our own. The viewsexpressed herein are solely the responsibility of the authors and should not be interpreted as reflecting theviews of the Federal Reserve Bank of San Francisco, the Board of Governors of the Federal Reserve System orthe Deutsche Bundesbank.
†Federal Reserve Bank of San Francisco; and Department of Economics, University of California, Davis(oscar.jorda@sf.frb.org; ojorda@ucdavis.edu).
‡Deutsche Bundesbank (katharina.knoll@bundesbank.de).§Department of Economics, University of Bonn (dmitry.kuvshinov@uni-bonn.de).¶Department of Economics, University of Bonn; and CEPR (moritz.schularick@uni-bonn.de).zDepartment of Economics and Graduate School of Management, University of California, Davis; NBER;
and CEPR (amtaylor@ucdavis.edu).
mailto:oscar.jorda@sf.frb.orgmailto:ojorda@ucdavis.edumailto:katharina.knoll@bundesbank.demailto:dmitry.kuvshinov@uni-bonn.demailto:moritz.schularick@uni-bonn.demailto:amtaylor@ucdavis.edu
1. Introduction
What is the rate of return in an economy and why do we care? The question is as old as the
economics profession itself. David Ricardo and John Stuart Mill devoted much of their time to the
study of interest and profits, while Karl Marx famously built his political economy in Das Kapital onthe idea that the profit rate tends to fall over time. Today, in our most fundamental economic theories,
the real risk-adjusted returns on different asset classes reflect equilibrium resource allocations given
society’s investment and consumption choices over time. Yet much more can be said beyond this
observation. Current debates on inequality, secular stagnation, risk premiums, and the natural rate,
to name a few, are all informed by conjectures about the trends and cycles in rates of return.
For all the abundance of theorizing, however, evidence has remained scant. Keen as we are to
empirically evaluate many of these theories and hypotheses, to do so with precision and reliability
obviously requires long spans of data. Our paper introduces, for the first time, a large annual dataset
on total rates of return on all major asset classes in the advanced economies since 1870—including
for the first-time total returns to the largest but oft ignored component of household wealth, housing.
Housing wealth is on average roughly one half of national wealth in a typical economy, and can
fluctuate significantly over time (Piketty, 2014) . But there is no previous rate of return database
which contains any information on housing returns. Here we build on prior work on house prices
(Knoll, Schularick, and Steger, 2017) and new data on rents (Knoll, 2016) to offer an augmented
database to track returns on this very important component of the national capital stock.
Thus, our first main contribution is to document our new and extensive data collection effort in
the main text and in far more detail in an extensive companion appendix.
We have painstakingly compiled annual asset return data for 16 advanced countries, over nearly
150 years. We construct three types of returns: investment income (i.e., yield), capital gains (i.e.,
price changes), and total returns (i.e., the sum of the two). These calculations were done for
four major asset classes, two of them risky—equities and housing—and two of them relatively
safe—government bonds and bills. Along the way, we have also brought in auxiliary sources to
validate our data externally. Our data consist of actual asset returns taken from market data. In
that regard, our data are therefore more detailed than returns inferred from wealth estimates in
discrete benchmark years as in Piketty (2014). We also follow earlier work in documenting annual
equity, bond, and bill returns, but here again we have taken the project further. We re-compute all
these measures from original sources, improve the links across some important historical market
discontinuities (e.g., closures and other gaps associated with wars and political instability), and in a
number of cases we access new and previously unused raw data sources. Our work thus provides
researchers with the first non-commercial database of historical equity, bond, and bill returns, with
the most extensive coverage across both countries and years, and the evidence drawn from our data
will establish new foundations for long-run macro-financial research.
Indeed, our second main contribution is to uncover fresh and unexpected stylized facts which
bear on active research debates, showing how our data offer fertile ground for future enquiry.
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In one contentious area of research, the accumulation of capital, the expansion of capital’s share
in income, and the growth rate of the economy relative to the rate of return on capital all feature
centrally in the current debate sparked by (Piketty, 2014) on the evolution of wealth, income, and
inequality. What do the long-run patterns on the rates of return on different asset classes have to
say about these possible drivers of inequality?
Another strand of research, triggered by the financial crisis and with roots in Alvin Hansen’s
(1939) AEA Presidential Address, seeks to revive the secular stagnation hypothesis (Summers, 2014).
Demographic trends are pushing the world’s economies into uncharted territory. We are living
longer and healthier lives and spending more time in retirement. The relative weight of borrowers
and savers is changing and with it the possibility increases that the interest rate will fall by an
insufficient amount to balance saving and investment at full employment. Are we now, or soon to
be, in the grip of another period of secular stagnation?
In a third major strand of financial research, preferences over current versus future consumption,
and attitudes toward risk, manifest themselves in the premiums that the rates of return on risky assets
carry over safe assets. A voluminous literature followed the seminal work of Mehra and Prescott
(1985). Returns on different asset classes, their volatilities, their correlations with consumption, and
with each other, sit at the core of the canonical consumption-Euler equation that underpins asset
pricing theories, and more broadly, the demand side of an aggregate economy in all standard macro
models. But tensions remain between theory and data, prompting further explorations of new asset
pricing paradigms including behavioral finance. Our new data adds another risky asset class to
the mix, housing. Along with equities, and when compared against the returns on bills and bonds,
can our new data provide new tests to compare and contrast alternative paradigms, some of which
depend on rarely observed events that require samples over long spans of time?
Lastly, in the sphere of monetary economics, Holston, Laubach, and Williams (2017) show that
estimates of the natural rate of interest in several advanced economies have gradually declined over
the past four decades and are now near zero. As a result, the probability that the nominal policy
interest rate may be constrained by the effective lower bound has increased, raising questions about
the prevailing policy framework. In this regard, how frequent and persistent are such downturns in
the natural rate and could there be a need for our monetary policy frameworks to be revised?
The common thread running through each of these broad research topics is the notion that the
rate of return is central to understanding long-, medium-, and short-run economic fluctuations. But
which rate of return? And how do we measure it? The risky rate is a measure of profitability of
private investment. The safe rate plays an important role in benchmarking compensation for risk,
and is often tied to discussions of monetary policy settings and the notion of the natural rate.
Our paper follows a long and venerable tradition of economic thinking about fundamental
returns on capital that includes, among others, Adam Smith, Knut Wicksell, and John Maynard
Keynes. More specifically, our paper is closely related, and effectively aims to bridge the gap,
between two literatures. The first is rooted in finance and is concerned with long-run returns on
different assets. The literature on historical asset price returns and financial markets is too large to
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discuss in detail, but important contributions have been made with recent digitization of historical
financial time series, such as the project led by William Goetzmann and Geert Rouwenhorst at
Yale’s International Center for Finance. The book Triumph of the Optimists by Dimson, Marsh, andStaunton (2009) probably marked the first comprehensive attempt to document and analyze long-run
returns on investment for a broad cross-section of countries. Another key contribution to note is the
pioneering and multi-decade project to document the history of interest rates by Homer and Sylla
(2005).
The second related strand of literature is the analysis of comparative national balance sheets over
time, as in Goldsmith (1985). More recently, Piketty and Zucman (2014) have brought together data
from national accounts and other sources tracking the development of national wealth over long
time periods. They also calculate rates of return on capital by dividing aggregate capital income the
national accounts by the aggregate value of capital, also from national accounts. Our work is both
complementary and supplementary to theirs. It is complementary as the asset price perspective
and the national accounts approach are ultimately tied together by accounting rules and identities.
Using market valuations, we are able to corroborate and improve the estimates of returns on capital
that matter for wealth inequality dynamics. Our long-run return data are also supplementary to
the work of Piketty and Zucman (2014) in the sense that we quadruple the number of countries for
which we can calculate real rates of return, enhancing the generality of our findings.
Major findings We summarize our four main findings as follows.
1. On risky returns, rrisky Until this paper, we have had no way to know rates of return onall risky assets in the long run. Research could only focus on the available data on equity
markets (Campbell, 2003; Mehra and Prescott, 1985). We uncover several new stylized facts.
In terms of total returns, residential real estate and equities have shown very similar and
high real total gains, on average about 7% per year. Housing outperformed equity before
WW2. Since WW2, equities have outperformed housing on average, but only at the cost of
much higher volatility and higher synchronicity with the business cycle. The observation
that housing returns are similar to equity returns, yet considerably less volatile, is puzzling.
Diversification with real estate is admittedly harder than with equities. Aggregate numbers
do obscure this fact although accounting for variability in house prices at the local level still
appears to leave a great deal of this housing puzzle unresolved.
Before WW2, the real returns on housing and equities (and safe assets) followed remarkably
similar trajectories. After WW2 this was no longer the case, and across countries equities then
experienced more frequent and correlated booms and busts. The low covariance of equity and
housing returns reveals significant aggregate diversification gains (i.e., for a representative
agent) from holding the two asset classes. Absent the data introduced in this paper, economists
had been unable to quantify these gains.
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One could add yet another layer to this discussion, this time by considering international
diversification. It is not just that housing returns seem to be higher on a rough, risk-adjusted
basis. It is that, while equity returns have become increasingly correlated across countries over
time (specially since WW2), housing returns have remained uncorrelated. Again, international
diversification may be even harder to achieve than at the national level. But the thought
experiment suggests that the ideal investor would like to hold an internationally diversified
portfolio of real estate holdings, even more so than equities.
2. On safe returns, rsa f e We find that the real safe asset return has been very volatile overthe long-run, more so than one might expect, and oftentimes even more volatile than real
risky returns. Each of the world wars was (unsurprisingly) a moment of very low safe rates,
well below zero. So was the 1970s inflation and growth crisis. The peaks in the real safe rate
took place at the start of our sample, in the interwar period, and during the mid-1980s fight
against inflation. In fact, the long decline observed in the past few decades is reminiscent of
the decline that took place from 1870 to WW1. Viewed from a long-run perspective, it may
be fair to characterize the real safe rate as normally fluctuating around the levels that we see
today, so that today’s level is not so unusual. Consequently, we think the puzzle may well bewhy was the safe rate so high in the mid-1980s rather than why has it declined ever since.
Safe returns have been low on average, falling in the 1%–3% range for most countries and
peacetime periods. While this combination of low returns and high volatility has offered a
relatively poor risk-return trade-off to investors, the low returns have also eased the pressure
on government finances, in particular allowing for a rapid debt reduction in the aftermath of
WW2.
How do the trends we expose inform current debates on secular stagnation and economic
policy more generally? International evidence in Holston, Laubach, and Williams (2017) on
the decline of the natural rate of interest since the mid-1980s is consistent with our richer
cross-country sample. This observation is compatible with the secular stagnation hypothesis,
whereby the economy can fall into low investment traps (see, for example Summers, 2014) and
Eggertsson and Mehrotra (2014). More immediately, the possibility that advanced economies
are entering an era of low real rates calls into question standard monetary policy frameworks
based on an inflation target. Monetary policy based on inflation targeting had been credited
for the Great Moderation, until the Global Financial Crisis. Since that turbulent period,
the prospect of long stretches constrained by the effective lower bound have commentators
wondering whether inflation targeting regimes are the still the right approach for central
banks (Williams, 2016).
3. On the risk premium, rrisky − rsa f e Over the very long run, the risk premium has beenvolatile. A vast literature in finance has typically focused on business-cycle comovements in
short span data (see, for example Cochrane, 2009, 2011). Yet our data uncover substantial
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swings in the risk premium at lower frequencies that sometimes endured for decades, and
which far exceed the amplitudes of business-cycle swings.
In most peacetime eras this premium has been stable at about 4%–5%. But risk premiums
stayed curiously and persistently high from the 1950s to the 1970s, persisting long after the
conclusion of WW2. However, there is no visible long-run trend, and mean reversion appears
strong. Curiously, the bursts of the risk premium in the wartime and interwar years were
mostly a phenomenon of collapsing safe rates rather than dramatic spikes in risky rates.
In fact, the risky rate has often been smoother and more stable than safe rates, averaging
about 6%–8% across all eras. Recently, with safe rates low and falling, the risk premium has
widened due to a parallel but smaller decline in risky rates. But these shifts keep the two rates
of return close to their normal historical range. Whether due to shifts in risk aversion or other
phenomena, the fact that safe rates seem to absorb almost all of these adjustments seems like
a puzzle in need of further exploration and explanation.
4. On returns minus growth, rwealth− g Turning to real returns on all investable wealth, Piketty(2014) argued that, if the return to capital exceeded the rate of economic growth, rentiers
would accumulate wealth at a faster rate and thus worsen wealth inequality. Comparing
returns to growth, or “r minus g” in Piketty’s notation, we uncover a striking finding. Evencalculated from more granular asset price returns data, the same fact reported in Piketty (2014)
holds true for more countries and more years, and more dramatically: namely “r � g.”
In fact, the only exceptions to that rule happen in very special periods: the years in or right
around wartime. In peacetime, r has always been much greater than g. In the pre-WW2period, this gap was on average 5% per annum (excluding WW1). As of today, this gap is still
quite large, in the range of 3%–4%, and it narrowed to 2% during the 1970s oil crises before
widening in the years leading up to the Global Financial Crisis.
However, one puzzle that emerges from our analysis is that while “r minus g” fluctuates overtime, it does not seem to do so systematically with the growth rate of the economy. This
feature of the data poses a conundrum for the battling views of factor income, distribution,
and substitution in the ongoing debate (Rognlie, 2015). Further to this, the fact that returns to
wealth have remained fairly high and stable while aggregate wealth increased rapidly since
the 1970s, suggests that capital accumulation may have contributed to the decline in the labor
share of income over the recent decades (Karabarbounis and Neiman, 2014). In thinking about
inequality and several other characteristics of modern economies, the new data on the return
to capital that we present here should spur further research.
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2. A new historical global returns database
The dataset unveiled in this study covers nominal and real returns on bills, bonds, equities, and
residential real estate in 16 countries from 1870 to 2015. The countries covered are Australia, Belgium,
Denmark, Finland, France, Germany, Italy, Japan, the Netherlands, Norway, Portugal, Spain, Sweden,
Switzerland, the United Kingdom, and the United States. Table 1 summarizes the data coverage by
country and asset class.
In this section, we will discuss the main sources and definitions for the calculation of long-run
returns. A major innovation is the inclusion of housing. Residential real estate is the main asset in
most household portfolios, as we shall see, but so far very little has been known about long-run
returns on housing.
Like most of the literature, we examine returns to national aggregate holdings of each asset
class. Theoretically, these are the returns that would accrue for the hypothetical representative-agent
investor holding each country’s portfolio. Within country heterogeneity is undoubtedly important,
but clearly beyond the scope of a study covering nearly 150 years of data and 16 advanced economies.
Table 1: Data coverage
Country Bills Bonds Equities HousingAustralia 1870–2015 1900–2015 1870–2015 1901–2015Belgium 1870–2015 1870–2015 1870–2015 1890–2015Denmark 1875–2015 1870–2015 1893–2015 1876–2015Finland 1870–2015 1870–2015 1896–2015 1920–2015France 1870–2015 1870–2015 1870–2015 1871–2015Germany 1870–2015 1870–2015 1870–2015 1871–2015Italy 1870–2015 1870–2015 1870–2015 1928–2015Japan 1876–2015 1881–2015 1886–2015 1931–2015Netherlands 1870–2015 1870–2015 1900–2015 1871–2015Norway 1870–2015 1870–2015 1881–2015 1871–2015Portugal 1880–2015 1871–2015 1871–2015 1948–2015Spain 1870–2015 1900–2015 1900–2015 1901–2015Sweden 1870–2015 1871–2015 1871–2015 1883–2015Switzerland 1870–2015 1900–2015 1900–2015 1902–2015UK 1870–2015 1870–2015 1871–2015 1900–2015USA 1870–2015 1871–2015 1872–2015 1891–2015
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2.1. The composition of wealth
Table 2 and Figure 1 show the decomposition of economy-wide investable asset holdings and capital
stock average shares across five major economies at the end of 2015: France, Germany, Japan, UK
and USA. Investable assets, displayed on the left panel of Figure 1, exclude assets that relate to
intra-financial holdings and cannot be held directly by investors, such as loans, derivatives (apart
from employee stock options), financial institutions’ deposits, insurance and pension claims.1 That
leaves housing, other non-financial assets—mainly other buildings, machinery, and equipment—
equity, bonds, bills, deposits and other financial assets, which mainly include private debt securities
(corporate bonds and asset-backed securities). The right panel of Figure 1 shows the decomposition
of the capital stock into housing and various other non-financial assets. The decomposition of
investable assets into individual classes for each country, is further shown in Table 2.
Housing, equity, bonds, and bills comprise over half of all investable assets in the advanced
economies today (nearly two-thirds whenever deposit rates are added). The housing returns data
also allow us to assess returns on around half of the outstanding total capital stock, using our new
total return series as a proxy for aggregate housing returns. Our improved and extended equity
return data for publicly-traded equities will then be used, as is standard, as a proxy for aggregate
business equity returns.2
2.2. Historical return data
Our measure of the bill return, the canonical risk-free rate, is taken to be the yield on Treasury bills,
i.e., short-term, fixed-income government securities. The yield data come from the latest vintage of
the long-run macrohistory database (Jordà, Schularick, and Taylor, 2016b).3 For periods when data
on Treasury bill returns were unavailable, we relied on either money market rates or deposit rates of
banks from Zimmermann (2017).
Our measure of the bond return is taken to be the the total return on long-term government
bonds. Unlike a number of preceding cross-country studies, we focus on the bonds listed and traded
on local exchanges, and denominated in local currency. The focus on local-exchange bonds makes
the bond return estimates more comparable to those of equities, housing and bills. Further, this
results in a larger sample of bonds, and focuses our attention on those bonds that are more likely to
be held by the representative household in the respective country. For some countries and periods
we have made use of listings on major global exchanges to fill gaps where domestic markets were
thin, or local exchange data were not available (for example, Australian bonds listed in New York or
1Both decompositions also exclude human capital, which cannot be bought or sold. Lustig, Van Nieuwer-burgh, and Verdelhan (2013) show that for a broader measure of aggregate wealth that includes humancapital, the size of human wealth is larger than of non-human wealth, and its return dynamics are similar tothose of a long-term bond.
2For example, to proxy the market value of unlisted equities, the US Financial Accounts apply industry-specific stock market valuations to the net worth and revenue of unlisted companies.
3www.macrohistory.net/data
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www.macrohistory.net/data
Table 2: Composition of investable assets by country
Country Housing Equity Bonds Bills Deposits Other Other
financial non-financialFrance 23.2 28.0 5.1 1.5 10.4 11.9 19.8Germany 22.2 24.2 5.6 0.2 14.0 17.3 16.4Japan 10.9 13.4 13.1 1.5 18.9 12.9 29.4UK 27.5 24.8 6.1 0.2 10.7 12.6 18.1USA 13.3 39.1 8.6 0.8 7.3 11.2 19.8Average share 19.4 25.9 7.7 0.9 12.3 13.2 20.7
Note: Ratios to total investable assets, percentage points. End-2015. Data are sourced from national accountsand national wealth estimates published by the countries’ central banks and statistical offices.
Figure 1: Composition of investable assets and capital stock in the major economies
Housing
Equity
Bonds
BillsDeposits
Other financial
Other non-financial
Investable Assets
Housing
Other Buildings
MachineryOther
Capital Stock
Note: Composition of total investable assets and capital stock. Average of the individual asset shares of France,Germany, Japan, UK and US, end-2015. Investable assets are defined as the gross total of economy-wideassets excluding loans, derivatives, financial institutions’ deposits, insurance, and pension claims. The capitalstock is business capital plus housing. Data are sourced from national accounts and national wealth estimatespublished by the countries’ central banks and statistical offices.
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London). Throughout the sample we target a maturity of around 10 years. For the second half of the
20th century, the maturity of government bonds is generally accurately defined. For the pre-WW2
period we sometimes had to rely on data for perpetuals, i.e., very long-term government securities
(such as the British consol).
Our dataset also tracks the development of returns on equity and housing. The new data on
total returns on equity come from a broad range of sources, including articles in economic and
financial history journals, yearbooks of statistical offices and central banks, stock exchange listings,
newspapers, and company reports. Throughout most of the sample, we rely on indices weighted by
market capitalization of individual stocks, and a stock selection that is representative of the entire
stock market. For some historical time periods in individual countries, however, we also make use
of indices weighted by company book capital, stock market transactions, or weighted equally, due
to limited data availability.
To the best of the authors’ knowledge, this study is the first to present long-run returns on
residential real estate. We combine the long-run house price series presented by Knoll, Schularick,
and Steger (2017) with a novel dataset on rents from Knoll (2016). For most countries, the rent
series rely on the rent components of the cost of living of consumer price indices as constructed by
national statistical offices and combine them with information from other sources to create long-run
series reaching back to the late 19th century.
We also study a number of “composite” asset returns, as well as those on the individual asset
classes—bills, bonds, equities and housing—described above. More precisely, we compute the rate of
return on safe assets, risky assets, and aggregate wealth, as weighted averages of the individual asset
returns. To obtain a representative return from the investor’s perspective, we use the outstanding
stocks of the respective asset in a given country as weights. To this end, we make use of new data on
equity market capitalization (from Kuvshinov and Zimmermann, 2017) and housing wealth for each
country and period in our sample, and combine them with existing estimates of public debt stocks
to obtain the weights for the individual assets. A graphical representation of these asset portfolios,
and further description of their construction is provided in the Appendix Section E.
Tables A.14 and A.15 present an overview of our four asset return series by country, their main
characteristics and coverage. The paper comes with an extensive data appendix that specifies the
sources we consulted and discusses the construction of the series in greater detail (see the Data
Appendix, Section K for housing returns, and Section L for equity and bond returns).
2.3. Calculating returns
The total annual return on any financial asset can be divided into two components: the capital gain
from the change in the asset price P, and a yield component Y, that reflects the cash-flow return onan investment. The total nominal return R for asset i in country j at time t is calculated as:
Total return: Ri,j,t =Pi,j,t − Pi,j,t−1
Pi,j,t−1+ Yi,j,t . (1)
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Because of wide differences in inflation across time and countries, it is helpful to compare
returns in real terms. Let πj,t = (CPIi,j,t − CPIi,j,t−1)/CPIi,j,t−1 be the realized consumer price index(CPI) inflation rate in a given country j and year t. We calculate inflation-adjusted real returns r foreach asset class as
Real return: ri,j,t = (1 + Ri,j,t)/(1 + πj,t)− 1 . (2)
These returns will be summarized in period average form, by country, or for all countries.4
Investors must be compensated for risk to invest in risky assets. A measure of this “excess
return” can be calculated by comparing the real total return on the risky asset with the return on a
risk-free benchmark—in our case, the government bill rate, rbill,j,t. We therefore calculate the excessreturn ER for the risky asset i in country j as
Excess return: ERi,j,t = ri,j,t − rbill,j,t . (3)
In addition to individual asset returns, we also present a number of weighted “composite”
returns aimed at capturing broader trends in risky and safe investments, as well as the “overall
return” or “return on wealth.” Appendix E provides further details on the estimates of country
asset portfolios from which we derive country-year specific weights.
For safe assets, we assume that total public debt is divided equally into bonds and bills to proxy
the bond and bill stocks, since we have no data yet on the market weights (only total public debt
weight) over our full sample. The safe asset return is then computed as an average of the real returns
on bonds and bills as follows:
Safe return: rsa f e,j,t =rbill,j,t + rbond,j,t
2. (4)
For risky assets, the weights w here are the asset holdings of equity and housing stocks in therespective country j and year t, scaled to add to 1. We use stock market capitalization and housingwealth as weights for equity and housing. The risky asset return is a weighted average of returns on
equity and housing:
Risky return: rrisky,j,t = requity,j,t × wequity,j,t + rhousing,t × whousing,j,t. (5)
The difference between our risky and safe return measures then provides a proxy for the
aggregate risk premium in the economy:
Risk premium: RPj,t = rrisky,j,t − rsa f e,j,t . (6)
4In what follows we focus on conventional average annual real returns. In addition, we often report period-average geometric mean returns corresponding to the annualized return that would be achieved through
reinvestment or compounding. These are calculated as(∏i∈T(1 + ri,j,t)
) 1T − 1. Note that the arithmetic period-
average return is always larger than the geometric period-average return, with the difference increasing withthe volatility of the sequence of returns.
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The “return on wealth” measure is a weighted average of returns on risky assets (equity and
housing) and safe assets (bonds and bills). The weights w here are the asset holdings of risky andsafe assets in the respective country j and year t, scaled to add to 1.
Return on wealth: rwealth,j,t = rrisky,j,t × wrisky,j,t + rsa f e,t × wsa f e,j,t. (7)
For comparison, Appendix Section F also provides information on the equally-weighted risky
return, and the equally-weighted rate of return on wealth, that are simple averages of housing and
equity, and housing, equity and bonds respectively.
Finally, we also consider returns from a global investor perspective in Appendix Section G.
These measure the returns from investing in local markets in US dollars. This measure effectively
subtracts the depreciation of the local exchange rate vis-a-vis the dollar from the nominal return:
USD return: RUSDi,j,t = Ri,j,t − ∆sj,t, (8)
where ∆sj,t is the depreciation of the local exchange rate vis-a-vis the US dollar in year tThe real USD returns are then computed net of US inflation πUSA,t:
Real USD return: rUSDi,j,t = (1 + RUSDi,j,t )/(1 + πUSA,t)− 1, (9)
2.4. Constructing housing returns using the rent-price approach
This section briefly describes our methodology to calculate total housing returns, and we provide
further details as needed later in the paper (Section 6.2 and Appendix Section K).
We construct estimates for total returns on housing using the rent-price approach. This approach
starts from a benchmark rent-price ratio (RI0/HPI0) estimated in a baseline year (t = 0). For thisratio we rely on net rental yields the Investment Property Database (IPD).56 We can then construct a
time series of returns by combining separate information from a country-specific house price index
series (HPIt/HPI0) and a country-specific rent index series (RIt/RI0). For these indices we rely onprior work on housing prices (Knoll, Schularick, and Steger, 2017) and new data on rents (Knoll,
2016). This method assumes that the indices cover a representative portfolio of houses. If so, there is
no need to correct for changes in the housing stock, and only information about the growth rates in
prices and rents is necessary.
5Net rental yields use rental income net of maintenance costs, ground rent and other irrecoverableexpenditure. We use net rather than gross yields to improve comparability with other asset classes.
6For Australia, we use the net rent-price ratio from Fox and Tulip (2014). For Belgium, we construct a grossrent-price ratio using data from Numbeo.com, and scale it down to account for running costs and depreciation.Both of these measures are more conservative than IPD, and more in line with the alternative benchmarks forthese two countries.
11
Numbeo.com
Given the above, a time series of the rent-to-price ratio can be derived iteratively as
RIt+1HPIt+1
=
[(RIt+1/RIt)
(HPIt+1/HPIt)
]RIt
HPIt. (10)
In a second step, returns on housing can be computed as:
Rhouse,t+1 =RIt+1HPIt
+HPIt+1 − HPIt
HPIt. (11)
As this approach is sensitive to the choice of rent-price-ratio at benchmark dates, we corroborate
the plausibility of the historical rent-price ratios with additional primary sources as well as economic
and financial history books and articles. Where the rent-price approach estimates diverge from
the alternative historical sources, we additionally benchmark the ratio to historical estimates of net
rental yields. We also examine the sensitivity of aggregate return estimates to varying benchmark
ratio assumptions. For further details, see Section 6.2 and Appendix Section K.
3. Rates of return: Aggregate trends
We begin with the first key finding—one that was completely unknown until now, due to lack of
evidence. The data summary in Table 3 and Figure 2 show that residential real estate, not equity,
has been the best long-run investment over the course of modern history. The full sample summary
return data are shown in the upper panel of Table 3, and the post-1950 sample in the bottom panel.
Data are pooled and equally-weighted, i.e., they are raw rather than portfolio returns. We include
wars so that results are not polluted by omitted disasters. We do, however, exclude hyperinflations
in order to focus on the underlying trends in returns, rather than inflation.
Although returns on housing and equities are similar, the volatility of housing returns is
substantially lower, as Table 3 shows. Returns on the two asset classes are in the same ballpark—
around 7%—but the standard deviation of housing returns is substantially smaller than that of
equities (10% for housing versus 22% for equities). Predictably, with thinner tails, the compounded
return (using the geometric average) is vastly better for housing than for equities—6.6% for housing
versus 4.6% for equities. This finding appears to contradict one of the basic assumptions of modern
valuation models: higher risks should come with higher rewards.
We can see that differences in asset returns are not driven by unusual events in the early pre-
WW2 part of our long historical sample. The bottom half of Table 3 makes this point. Compared to
the full sample period (1870–2015) reported in the upper half of the table, the same clear pattern
emerges: stocks and real estate dominate in terms of returns. Moreover, average returns post–1950
are similar to the full sample, even though the later period excludes the devastating effects of the
two world wars.
Other robustness checks are reported in the Appendix in Figures A.1, A.2, and A.3. Briefly, we
find that the observed patterns are not driven by the smaller European countries in our sample.
12
Table 3: Global real returns
Real returns Nominal Returns
Bills Bonds Equity Housing Bills Bonds Equity Housing
Full sample:
Mean return p.a. 0.98 2.50 6.89 7.05 4.60 6.10 10.75 11.06Std.dev. 6.01 10.74 21.94 9.98 3.33 8.91 22.78 10.70Geometric mean 0.78 1.94 4.64 6.61 4.55 5.74 8.55 10.59Mean excess return p.a. . 1.53 5.91 6.07Std.dev. . 8.38 21.43 9.86Geometric mean . 1.19 3.81 5.64Observations 1739 1739 1739 1739 1739 1739 1739 1739
Post-1950:
Mean return p.a. 0.87 2.77 8.28 7.44 5.40 7.31 12.99 12.31Std.dev. 3.43 9.94 24.20 8.88 4.04 9.80 25.09 10.15Geometric mean 0.81 2.30 5.54 7.10 5.33 6.89 10.28 11.90Mean excess return p.a. . 1.91 7.41 6.57Std.dev. . 9.20 23.77 9.19Geometric mean . 1.51 4.79 6.21Observations 1016 1016 1016 1016 1016 1016 1016 1016
Note: Annual global returns in 16 countries, equally weighted. Period coverage differs across countries.Consistent coverage within countries. Excess returns are computed relative to bills.
Figure 2: Global real rates of return
Bills
Bonds
Equity
Housing
0 2 4 6 8Mean annual return, per cent
Full sample
Bills
Bonds
Equity
Housing
0 2 4 6 8Mean annual return, per cent
Post-1950
Excess Return vs Bills Mean Annual Return
Notes: Arithmetic avg. real returns p.a., unweighted, 16 countries. Consistent coverage within each country.
13
Figure A.1 shows average real returns weighted by country-level real GDP, both for the full sample
and post–1950 period. Compared to the unweighted averages, equity performs slightly better, but
the returns on equity and housing remain very similar, and the returns and riskiness of all four
asset classes are very close to the unweighted series in Table 3.
The results could be biased because different countries enter the sample at different dates due to
data availability. Figure A.2 plots the average returns for sample-consistent country groups, starting
at benchmark years—the later the benchmark year, the more countries we can include. Again, the
broad patterns discussed above are largely unaffected.
We also investigate the possibility that the results are biased because of wartime experiences.
We recompute average returns, but now dropping the two world wars from the sample. Figure A.3
plots the average returns in this case, and alas the main result remains largely unchanged. Appendix
Table A.3 also considers the risky returns during wartime in more detail, to assess the evidence
for rare disasters in our sample. Returns during both wars were indeed low and often negative,
although returns during World War 2 in a number of countries were relatively robust.
Finally, our aggregate return data take the perspective of a domestic investor in a representative
country. Appendix Table A.9 instead takes the perspective of a global US-Dollar investor, and
assesses the US-Dollar value of the corresponding returns. The magnitude and ranking of returns
are similar to those in Table 3 above, although the volatilities are substantially higher, as expected
given that the underlying asset volatility is compounded by that in the exchange rate. This higher
volatility is also reflected in somewhat higher levels of US-Dollar returns, compared to those in local
currency.
4. Safe rates of return
Figure 3 shows the trends in real returns on government bonds (solid line) and bills (dashed
line) since 1870. The global returns are GDP-weighted averages of the 16 countries in our sample.
Although we do not show the unweighted data, the corresponding figure would look very similar.
We smooth the data using a decadal moving average—for example, the observation reported in 1900
is the average of data from 1895 to 1905.
Two striking features of Figure 3 deserve comment. First, we can see that low real rates, and
in fact negative real rates have been relatively common during modern financial history. Second,
for the most part returns to long-term and short-term safe assets have tracked each other very
closely—with a premium of about 1% that has widened considerably since the well-documented
decline of the mid-1980s (Holston, Laubach, and Williams, 2017).
Safe rates are far from stable in the medium-term. There is enormous time series, as well as
cross-country variability. In fact, real safe rates appear to be as volatile (or even more volatile)
than real risky rates, a topic we return to in the next subsection. Considerable variation in the risk
premium often comes from sharp changes in safe real rates, not from the real returns on risky assets.
Two four-decade-long declines in real rates stand out: (1) from 1870 to WW1 (with a subsequent
14
Figure 3: Trends in real returns on bonds and bills
-6-3
03
69
Per
cen
t
1870 1890 1910 1930 1950 1970 1990 2010
Real bill rate: decadal moving averageReal bond return: decadal moving average
Note: Mean returns for 16 countries, weighted by real GDP. Decadal moving averages.
further collapse during the war); and (2) the well-documented decline that started in the mid-1980s.
Add to this list the briefer, albeit more dramatic decline that followed the Great Depression into
WW2. Some observers have therefore interpreted the recent downward trend in safe rates as a sign
of “secular stagnation” (see, for example Summers, 2014).
However, in contrast to 1870 and the late 1930s, the more recent decline is characterized by a
much higher term premium—a feature with few precedents in our sample. There are other periods
in which real rates remained low, such as in the 1960s. They were pushed below zero, particularly
for the longer tenor bonds, during the 1970s inflation spike, although here too term premiums
remained relatively tight. Returns dip dramatically during both world wars. It is perhaps to be
expected: demand for safe assets spikes during disasters although the dip may also reflect periods
of financial repression that usually emerge during times of conflict, and which often persist into
peacetime. Thus, from a broad historical perspective, high rates of return on safe assets and high
term premiums are more the exception than the rule.
Summing up, during the late 19th and 20th century, real returns on safe assets have been
low—on average 1% for bills and 2.5% for bonds—relative to alternative investments. Although
the return volatility—measured as annual standard deviation—is lower than that of housing and
equities, these assets offered little protection during high-inflation eras and during the two world
wars, both periods of low consumption growth.
15
Figure 4: Correlations across safe asset returns0
.2.4
.6.8
1
1870 1890 1910 1930 1950 1970 1990 2010
Bonds vs Bills
-.50
.51
1870 1890 1910 1930 1950 1970 1990 2010
Bonds (nom.) Bills (nominal)
Comovement with inflation
0.2
.4.6
.8
1870 1890 1910 1930 1950 1970 1990 2010
Bonds (real) Bills (real)
Cross-country comovement
Note: Rolling decadal correlations. The global correlation coefficient is the average of individual countries forthe rolling window. Cross-country correlation coefficient is the average of all country pairs for a given assetclass. Country coverage differs across time periods.
Figure 4 explores additional key moments of the data. The top-left panel plots the correlation
between real bond and bill returns, again using decadal rolling windows and computed as the
cross-sectional average of correlations. In parallel to our discussion of the term premium, real
returns on bonds and bills have been highly correlated for most of the sample up until the 1960s.
From the 1970s onwards, the era of fiat money and higher average inflation, this correlation has
become much weaker, and near zero at times, coinciding with a widening term premium.
The top right panel of Figure 4 displays the correlation between nominal safe asset returns and
inflation. The figure shows that safe assets provided more of an inflation hedge starting in the
1970s, around the start of the era of modern central banking. However, as Figure 3 showed, both
16
Table 4: Real rates of return on bonds and bills
Country Full Sample Post 1950 Post 1980
Bills Bonds Bills Bonds Bills BondsAustralia 1.29 2.24 1.32 2.45 3.23 5.85Belgium 1.16 3.01 1.50 3.86 2.30 6.24Denmark 3.08 3.58 2.18 3.50 2.80 7.13Finland 0.64 3.22 0.63 4.86 2.61 5.76France -0.47 1.54 0.95 2.96 2.22 6.94Germany 1.51 3.15 1.86 3.69 1.96 4.22Italy 1.20 2.53 1.30 2.83 2.42 5.85Japan 0.68 2.54 1.36 2.83 1.48 4.53Netherlands 1.37 2.71 1.04 2.14 2.08 5.59Norway 1.10 2.55 -0.26 1.94 1.50 5.62Portugal -0.01 2.23 -0.65 1.59 0.65 6.25Spain -0.04 1.41 -0.32 1.21 2.20 5.72Sweden 1.77 3.25 0.82 2.70 1.51 6.59Switzerland 0.89 2.41 0.12 2.33 0.33 3.35UK 1.16 2.29 1.14 2.63 2.70 6.67USA 2.17 2.79 1.30 2.64 1.71 5.71Average, unweighted 1.13 2.61 0.89 2.76 1.98 5.75Average, weighted 1.31 2.49 1.17 2.65 1.89 5.55
Note: Average annual real returns. Period coverage differs across countries. Consistent coverage withincountries. The average, unweighted and average, weighted figures are respectively the unweighted andreal-GDP-weighted arithmetic averages of individual country returns.
bonds and bills have experienced prolonged periods of negative real returns—both during wartime
inflation, and the high-inflation period of the late 1970s. Although safe asset rates usually comove
positively with inflation, they do not always compensate the investor fully.
The bottom panel of Figure 4 displays the cross correlation of safe returns over rolling decadal
windows to examine how much inflation risk can be diversified with debt instruments. This
correlation coefficient is the average of all country-pair combinations for a given window, and is
calculated as
Corri,t =∑j ∑k 6=j Corr(ri,j,t∈T, ri,k,t∈T)
∑j ∑k 6=j 1
for asset i (here: bonds or bills), and time window T = (t− 5, t + 5). Here j and k denote the countrypairs, and r denotes real returns, constructed as described in Section 2.3.
Cross-country real safe returns have exhibited positive comovement throughout history. The
degree of comovement shows a few marked increases associated with WW1 and the 1930s. The effect
of these major global shocks on individual countries seems to have resulted in a higher correlation
of cross-country asset returns. This was less true of WW2 and its aftermath, perhaps because the
evolving machinery of financial repression was better able to manage the yield curve.
Turning to cross-sectional features, Table 4 shows country-specific safe asset returns for three
17
Figure 5: Trends in real return on safe assets and GDP growth
-6-4
-20
24
68
Per
cen
t
1870 1890 1910 1930 1950 1970 1990 2010
Real safe return: decadal moving averageReal GDP growth: decadal moving average
Note: Mean returns and GDP growth for 16 countries, weighted by real GDP. Decadal moving averages. Thesafe rate of return is an arithmetic average of bonds and bills.
samples: all years, post–1950, and post–1980. Here the experiences of a few countries stand out.
In France, real bill returns have been negative when averaged over the full sample. In Portugal
and Spain, they have been approximately zero. In Norway, the average return on bills has been
negative for the post-1950 sample. However, most other countries have experienced reasonably
similar returns on safe assets, in the ballpark of 1%− 3%.Aside from the investor perspective discussed above, safe rates of return have important
implications for government finances, as they measure the cost of raising and servicing government
debt. What matters for this is not the level of real return per se, but its comparison to real GDPgrowth, or rsa f e− g. If the rate of return exceeds real GDP growth, rsa f e > g, reducing the debt/GDPratio requires continuous budget surpluses. When rsa f e is less than g, however, a reduction indebt/GDP is possible even with the government running modest deficits.
Figure 5 plots the representative “safe rate of return”—the arithmetic average of bond and bill
returns (dashed line)—against real GDP growth (solid line), again as decadal moving averages.
Starting in the late 19th century, safe rates were higher than GDP growth, meaning that any
government wishing to reduce debt had to run persistent budget surpluses. Indeed, this was the
strategy adopted by Britain to pay off the debt incurred during the Napoleonic War (Crafts, 2016).
The two world wars saw low real returns, but nevertheless a large debt accumulation to finance the
wartime effort. The aftermath of these two wars, however, offered vastly different experiences for
18
public finances. After World War 1, safe returns were high and growth—low, requiring significant
budgetary efforts to repay the war debts. This was particularly difficult given the additional
reparations imposed by the Treaty of Versailles, and the turbulent macroeconomic environment at
the time. After World War 2, on the contrary, high growth and inflation helped greatly reduce the
value of national debt, creating rsa f e − g gaps as large as –10 percentage points.More recently, the Great Moderation saw a reduction in inflation rates and a corresponding
increase in the debt financing burden, whereas the impact of rsa f e − g in the aftermath of the GlobalFinancial Crisis remains broadly neutral, with the two rates roughly equal. On average throughout
our sample, the real growth rate has been around 1 percentage point higher than the safe rate of
return (3% growth versus 2% safe rate), meaning that governments could run small deficits without
increasing the public debt burden.
In sum, real returns on safe assets, even adjusted for risk, have been quite low across the
advanced countries and throughout the last 150 years. In fact, for some countries, these returns have
been persistently negative. Periods of unexpected inflation, in war and peace, have often diluted
returns, and flights to safety have arguably depressed returns in the asset class even further in the
more turbulent periods of global financial history. The low return for investors has, on the flipside,
implied a low financing cost for governments, which was particularly important in reducing the
debts incurred during World War 2.
5. Risky rates of return
We next shift our focus to look at the risky assets in our portfolio, i.e., housing and equities. Figure
6 shows the trends in real returns on housing (solid line) and equity (dashed line) for our entire
sample, again presented as decadal moving averages. In addition, Figure 7 displays the correlation
of risky returns between asset classes, across countries, and with inflation, in a manner similar to
Figure 4.
A major stylized fact leaps out. Prior to WW2, real returns on housing, safe assets and equities
followed remarkably similar trajectories. After WW2 this was no longer the case. Risky returns were
high and stable in the 19th century, but fell sharply around WW1, with the decade-average real
equity returns turning negative. Returns recovered quickly during the 1920s, before experiencing a
reasonably modest drop in the aftermath the Great Depression. Most strikingly though, from the
onset of WW2 onwards the trajectories of the two risky asset classes diverged markedly from each
other, and also from those of safe assets.
Equity returns have experienced many pronounced global boom-bust cycles, much more so
than housing returns, with real returns as high as 16% and as low as −4% over the course of entiredecades. Equity returns fell in WW2, boomed sharply during the post-war reconstruction, and
fell off again in the climate of general macroeconomic instability in the late 1970s. Equity returns
bounced back following a wave of deregulation and privatization of the 1980s. The next major event
to consider was the Global Financial Crisis, which extracted its toll on equities and to some extent
19
Figure 6: Trends in real returns on equity and housing
-40
48
1216
Per
cen
t
1870 1890 1910 1930 1950 1970 1990 2010
Real equity return: decadal moving averageReal housing return: decadal moving average
Note: Mean returns for 16 countries, weighted by real GDP. Decadal moving averages.
housing, as we shall see.
Housing returns, on the other hand, have remained remarkably stable over the entire post-WW2
period. As a consequence, the correlation between equity and housing returns, depicted in the top
panel of Figure 7, was highly positive before WW2, but has all but disappeared over the past five
decades. The low covariance of equity and housing returns over the long run reveals attractive gains
from diversification across these two asset classes that economists, up to now, have been unable to
measure or analyze.
In terms of relative returns, housing persistently outperformed equity up until the end of WW1,
even though the returns followed a broadly similar temporal pattern. In recent decades, equities
have slightly outperformed housing on average, but only at the cost of much higher volatility and
cyclicality. Furthermore, the upswings in equity prices have generally not coincided with times
of low growth or high inflation, when standard theory would say high returns would have been
particularly valuable.
The top-right panel of Figure 7 shows that equity co-moved negatively with inflation in the
1970s, while housing provided a more robust hedge against rising consumer prices. In fact, apart
from the interwar period when the world was gripped by a general deflationary bias, equity returns
have co-moved negatively with inflation in almost all eras. Moreover, the big downswings in equity
returns in the two world wars and the 1970s coincided with periods of generally poor economic
20
Figure 7: Correlations across risky asset returns0
.2.4
.6
1870 1890 1910 1930 1950 1970 1990 2010
Equity vs Housing
-.4-.2
0.2
.4.6
1870 1890 1910 1930 1950 1970 1990 2010
Equity (nom.) Housing (nominal)
Comovement with inflation
-.20
.2.4
.6.8
1870 1890 1910 1930 1950 1970 1990 2010
Equity (real) Housing (real)
Cross-country comovement
Note: Rolling decadal correlations. The global correlation coefficient is the average of individual countries forthe rolling window. Cross-country correlation coefficient is the average of all country pairs for a given assetclass. Country coverage differs across time periods.
performance.
In the past two decades, equity returns have also become highly correlated across countries,
as shown by the sharp rise in the degree of comovement in the bottom-left panel of Figure 7. A
well-diversified global equity portfolio has become less of a hedge against country-specific risk
(Quinn and Voth, 2008). As is a matter of debate, this may reflect the greater trading across equity
markets globally, or an increase in the global shocks to which firms, especially those in the typical
equity index, are increasingly exposed. In contrast to equities, cross-country housing returns have
remained relatively uncorrelated, perhaps because housing assets remain less globally tradable than
equities or are exposed more to idiosyncratic country-level shocks.
21
Table 5: Real rates of return on equity and housing
Country Full Sample Post 1950 Post 1980
Equity Housing Equity Housing Equity HousingAustralia 7.81 6.37 7.57 8.29 8.78 7.16Belgium 6.23 7.89 9.65 8.14 11.49 7.20Denmark 7.22 8.10 9.33 7.04 12.57 5.14Finland 9.98 9.58 12.81 11.18 16.17 9.47France 3.25 6.54 6.38 10.38 11.07 6.39Germany 6.85 7.82 7.52 5.29 10.06 4.12Italy 7.32 4.77 6.18 5.55 9.45 4.57Japan 6.09 6.54 6.32 6.74 5.79 3.58Netherlands 7.09 7.28 9.41 8.53 11.90 6.41Norway 5.95 8.03 7.08 9.10 11.76 9.81Portugal 4.37 6.31 4.70 6.01 8.34 7.15Spain 5.46 5.21 7.11 5.83 11.00 4.62Sweden 7.98 8.30 11.30 8.94 15.74 9.00Switzerland 6.71 5.63 8.73 5.64 10.06 6.19UK 7.20 5.36 9.22 6.57 9.34 6.81USA 8.39 6.03 8.75 5.62 9.09 5.66Average, unweighted 6.60 7.25 8.24 7.46 10.68 6.42Average, weighted 7.04 6.69 8.13 6.34 8.98 5.39
Note: Average annual real returns. Period coverage differs across countries. Consistent coverage withincountries. The average, unweighted and average, weighted figures are respectively the unweighted andreal-GDP-weighted arithmetic averages of individual country returns.
Next we explore long-run risky returns in individual countries. Table 5 shows the returns on
equities and housing by country for the full sample and for the post–1950 and post–1980 subsamples.
Long-run risky asset returns for most countries are close to 6%–8% per year, a figure which we think
represents a robust and strong real return to risky capital.
Still, the figures also show an important degree of heterogeneity among individual countries.
Many of the countries that have experienced large political shocks show lower equity returns. This
is the case for Portugal and Spain which both underwent prolonged civil strife, and France which
undertook a wave of nationalizations in the aftermath of WW2. French equity returns are also
negatively affected by the fallout from the world wars, and the fallout from an oil crisis in the 1960s
(for more detail, see Blancheton, Bonin, and Le Bris, 2014; Le Bris and Hautcoeur, 2010). In contrast,
real equity returns in Finland have been as high as 10%, on average throughout the sample. Housing
returns also show considerable heterogeneity. Returns on housing have been high on average in
the Nordic countries, but low in Italy and Spain. The US risky asset returns fall roughly in the
middle of the country-specific figures, with equity returns slightly above average, and housing
returns—slightly below. Our estimates of the US housing returns are in line with those in Favilukis,
Ludvigson, and Van Nieuwerburgh (2017).7 The degree of heterogeneity and the relative ranking of
7Favilukis, Ludvigson, and Van Nieuwerburgh (2017) estimate a gross nominal return on US housing of9%—11%, based on three data sources going back to 1950s and 1970s. This implies a net real return of around5—7% (once inflation, maintenance and running costs are subtracted), in line with our estimates in Table 5.
22
Figure 8: Risk and return of equity and housing
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Note: Left panel: average real return p.a. and standard deviation. Right panel: Sharpe ratios, measuredas (r̄i − r̄bill)/σi, where i is the risky asset with r̄i mean return and σi standard deviation. 16 countries.Consistent coverage within each country.
returns is broadly similar when comparing the full sample to the post-1950 period.
This country-level evidence reinforces one of our main findings: housing has been as good a
long-run investment as equities, and possibly better. Housing has offered a similar return to equity
in the majority of countries and time periods. In the long-run, housing outperformed equities in
absolute terms in 6 countries, and equities outperformed housing in 5. Returns on the two assets
were about the same in the remaining 5 countries. After WW2, housing was the best-performing
asset class in 3 countries, and equities in 9.
However, although aggregate returns on equities exceed aggregate returns on housing for certain
countries and time periods, equities do not outperform housing in simple risk-adjusted terms.
Figure 8 compares the riskiness and returns of housing and equities for each country. The left panel
plots average annual real returns on housing (orange crosses) and equities (green circles) against
their standard deviation. The right panel shows the Sharpe ratios for equities (in dark green) and
housing (in orange) for each country in the sample.8 Housing provides a higher return per unit
of risk in each of the 16 countries in our sample, with Sharpe ratios on average more than double
those of equities.
8The Sharpe ratio is calculated as (r̄i − r̄bill)/σi, where i is the risky asset (housing or equity) with r̄i meanreturn and σi standard deviation.
23
5.1. Decomposition of returns
What explains the superior risk-adjusted performance of housing relative to equities? To gain
insights into this question, we separately analyze movements in capital gains and income yield as
shown in Tables 6 and 7. The table shows both arithmetic and geometric average world returns over
the entire sample and since 1950. Capital gain measures the return from price appreciation only.
Depending on the asset, other components of total returns measure income from either dividends
or rents received by the investor. Both capital gain and dividend or rental income are expressed as a
proportion of the previous period’s price. The small residual between combined capital gain and
dividend income, and the equity total return, accounts for gain and loss from capital operations
such as stock splits or share buybacks, and income from reinvestment of dividends.
Table 6 shows that the main reason risk-adjusted housing returns are higher is the lower
volatility of house prices. Both rental yields and dividend income are relatively stable for all years
and countries throughout the sample. However, the standard deviation of equity prices is double
that of house prices over the full sample, and around 2.5 times that of house prices after 1950.
Equity prices have experienced large swings and high-amplitude cycles throughout the course
of modern history. Moreover, capital gains—the more volatile component—are responsible for a
larger share of equity total returns than they are for housing. These two factors have become even
more relevant during the post-WW2 decades.
A similar pattern is visible at the country level, with the summary statistics shown in Table 7.
Table 6: Total nominal return components for equity and housing.
Full Sample Post 1950
Arithmetic Geometric Arithmetic GeometricHousing Capital gain 5.72 (10.42) 5.25 7.22 (9.82) 6.82
Rental income 5.49 (2.02) 5.47 5.26 (1.92) 5.24Total return 11.22 (10.76) 10.73 12.47 (10.23) 12.05Capital gain share 51% 49% 58% 57%
Equity Capital gain 6.62 (22.17) 4.46 9.17 (24.64) 6.47Dividend income 4.18 (1.80) 4.16 3.81 (1.89) 3.79Total return 10.81 (22.67) 8.63 13.00 (25.30) 10.24Capital gain share 61% 52% 71% 63%
Observations 1675 1675 985 985
Note: Average annual nominal returns across 16 countries, unweighted. Standard deviation in parentheses.Period coverage differs across countries. Consistent coverage within countries.
24
Table 7: Total nominal return components for equity and housing by country.
Housing Equity Obs.
Capitalgain
Rentalincome
Totalreturn
Capitalgainshare
Capitalgain
Dividendincome
Totalreturn
Capitalgainshare
Australia 6.53 4.03 10.56 61.85% 7.09 4.92 12.01 59.04% 113(13.72) (0.89) (13.81) (16.70) (1.08) (17.36)
Belgium 5.78 6.15 11.93 48.46% 6.84 3.83 10.67 64.11% 115(10.09) (1.46) (9.94) (23.73) (1.64) (24.35)
Denmark 4.95 6.90 11.85 41.80% 6.15 4.85 11.01 55.91% 123(7.93) (2.49) (8.11) (18.04) (2.24) (18.50)
Finland 8.72 7.19 15.91 54.82% 10.30 5.09 15.37 67.00% 91(14.70) (2.89) (15.74) (31.19) (1.98) (31.80)
France 7.49 5.25 12.73 58.80% 4.86 3.74 8.60 56.54% 136(9.28) (0.99) (9.73) (20.93) (1.34) (21.27)
Germany 3.50 6.03 9.52 36.73% 4.33 3.88 8.45 51.31% 111(10.20) (2.61) (10.85) (21.32) (1.60) (21.97)
Italy 7.29 3.49 10.77 67.63% 9.28 3.61 12.89 71.99% 81(14.74) (1.59) (15.03) (31.23) (1.30) (31.48)
Japan 5.89 4.70 10.60 55.60% 6.82 2.68 9.88 69.05% 70(9.60) (1.24) (9.97) (18.51) (1.76) (18.88)
Netherlands 5.25 5.96 11.21 46.86% 7.07 4.79 11.89 59.48% 84(8.59) (1.68) (9.14) (19.08) (1.58) (19.41)
Norway 4.62 6.72 11.34 40.76% 5.00 4.28 9.22 54.19% 135(8.08) (1.19) (8.31) (20.39) (1.62) (20.92)
Portugal 9.29 4.45 13.74 67.60% 8.49 2.54 11.05 76.86% 68(10.48) (1.74) (11.33) (36.03) (1.35) (36.41)
Spain 7.20 4.16 11.36 63.38% 6.86 4.65 11.29 60.74% 115(12.95) (1.60) (13.28) (19.83) (2.85) (20.65)
Sweden 4.23 7.20 11.43 36.98% 6.95 4.12 11.07 62.81% 130(7.52) (1.54) (7.90) (20.11) (1.03) (20.71)
Switzerland 3.85 4.64 8.49 45.31% 5.23 3.35 8.55 61.19% 70(6.17) (0.58) (6.23) (19.00) (1.44) (19.09)
UK 5.44 3.94 9.38 58.01% 6.42 4.75 11.25 57.12% 108(10.01) (0.88) (10.17) (21.53) (1.36) (22.39)
USA 3.54 5.33 8.87 39.94% 6.70 4.38 11.08 60.45% 125(8.24) (0.75) (8.40) (18.22) (1.57) (18.45)
Note: Arithmetic average of annual nominal returns, full sample. Standard deviation in parentheses. Periodcoverage differs across countries. Consistent coverage within countries.
25
The higher volatility of equity prices is a persistent feature of all countries and all periods in our
sample. Capital gains account for a relatively larger share of equity returns, compared to housing
returns, in 11 countries, and a similar share in 5 countries.
Since aggregate equity prices are subject to large and prolonged swings, a representative investor
would have to hold on to his equity portfolio for longer in order to ensure a high real return.
Aggregate housing returns, on the contrary, are more stable because swings in national house prices
are generally less pronounced. National aggregate housing portfolios have had comparable real
returns to national aggregate equity portfolios, but with only half the volatility.
6. Accuracy and comparability of risky returns
This section provides consistency and robustness checks by examining (1) the accuracy of equity
returns, (2) the accuracy of housing returns, and (3) the comparability of housing and equity returns.
6.1. Accuracy of equity returns
The literature on returns in equity markets has highlighted two main sources of bias in the data:
weighting and sample selection. Weighting biases arise from the fact that the stock portfolio weights
for the index do not correspond to those of a representative investor, or a representative agent in the
economy. Selection biases arise from the fact that the selection of stocks does not correspond to the
portfolio of the representative investor or agent. This second category also includes the issues of
survivorship bias and missing data bias arising from stock exchange closures and restrictions. We
consider how each of these biases may, or may not affect our equity return estimates in this section.
An accompanying Appendix Table A.15 also details the construction of the equity index for each
country and time period.
Weighting bias The best practice in weighting equity indices is to use market capitalizationof individual stocks. This approach most closely mirrors the composition of a hypothetical rep-
resentative investor’s portfolio. Equally-weighted indices are likely to overweight smaller firms,
which tend to carry higher returns and a higher risk. The existing evidence from historical returns
on the Brussels and Paris stock exchanges suggests that using equally-weighted indices biases
returns up by around 0.5 percentage points, and standard deviation up by 2–3 percentage points
(Annaert, Buelens, Cuyvers, De Ceuster, Deloof, and De Schepper, 2011; Le Bris and Hautcoeur,
2010). The size of the bias, however, is likely to vary across across markets and time periods. For
example, Grossman (2017) shows that the market-weighted portfolio of UK stocks outperformed its
equally-weighted counterpart over the period 1869–1929.
To minimize this bias, we use market-capitalization-weighted indices for the vast majority of our
sample (see Appendix Table A.15 and Section L). Where market-capitalization weighting was not
available, we have generally used alternative weights such as book capital or transaction volumes,
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rather than equally-weighted averages. For the few equally-weighted indices that remain in our
sample, the overall impact on aggregate return estimates ought to be negligible.
Selection and survivorship bias Relying on an index whose selection does not mirror therepresentative investor’s portfolio carries two main dangers. First, a small sample may be unrepre-
sentative of overall stock market returns. And second, a sample that is selected ad-hoc, and especially
ex-post, is likely to focus on surviving firms, or successful firms, thus overstating investment returns.
This second bias extends not only to stock prices but also to dividend payments, as some historical
studies only consider dividend-paying firms.9 The magnitude of survivor bias has generally been
found to be around 0.5 to 1 percentage points (Annaert, Buelens, and De Ceuster, 2012; Nielsen and
Risager, 2001), but in some time periods and markets it could be larger (see Le Bris and Hautcoeur,
2010, for the case of France).
As a first best, we always strive to use all-share indices that avoid survivor and selection biases.
For some countries and time periods where no such indices were previously available, we have
constructed new weighted all-share indices from original historical sources (e.g., early historical data
for Norway and Spain). Wh